Vehicle Weight Estimation Control Using Selected Driving-State Data
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Solution Overview
Problem
Existing vehicle control systems face challenges in accurately estimating vehicle weight, particularly with the expansion of self-driving technologies, car sharing services, and data services, which require improved estimation accuracy.
Innovation Solution
A vehicle control system that includes an information acquisition unit to gather braking or driving force and acceleration data, an information storage unit to store this data, and a weight estimation unit that derives an estimated vehicle weight based on pre-defined selection conditions to minimize estimation disturbances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle weight is estimated using all available traveling state information, then more data is utilized for estimation, but disturbance in estimation increases due to inclusion of noisy or irrelevant data
Solution Approach 1:
The patent extracts and selects only specific traveling state information that satisfies predetermined selection conditions from the complete set of available data. This selective extraction removes noisy or irrelevant information that would otherwise increase estimation disturbance, thereby improving both accuracy and reliability of vehicle weight estimation.
Solution Approach 2:
The patent applies different quality criteria (selection conditions) to different pieces of traveling state information based on their reliability and relevance. By evaluating each data point against specific conditions (such as vehicle state, sensor reliability, data quality metrics), the system ensures that only high-quality information contributes to the estimation, optimizing the local quality of input data.
2Measurement precision
If strict selection conditions are applied to filter traveling state information, then estimation disturbance is reduced, but the amount of usable data decreases
Solution Approach 1:
The patent dynamically adjusts selection conditions and thresholds based on changing vehicle states, operational contexts, and data quality metrics. By modifying parameters such as selection criteria strictness, time windows, and data thresholds according to current conditions, the system optimizes the balance between filtering out noisy data and retaining sufficient usable information for accurate estimation.
Data Source
AI summary
A vehicle control system includes an information acquisition unit that acquires traveling state information each time, an information storage unit that stores the traveling state information acquired by the information acquisition unit, and a weight estimation unit that derives an estimated value of a vehicle weight based on traveling state information satisfying a predetermined selection condition among a plurality of pieces of traveling state information stored by the information storage unit. The selection condition is a condition under which disturbance in derivation of the estimated value of the vehicle weight by the weight estimation unit is reduced.